Online Course Catalog
Jülich | Data Analysis | Online |
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ANDA: Correlation analysis of parallel massively spike trainsLecture recording during the workshop Advanced Neural Data Analysis (ANDA) |
Institution(s) |
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Lecturer(s) | Sonja Grün | |
Time / ETCS | / | |
Weblink | Visit the course website |
Jülich | Data Analysis | Online |
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ANDA: Correlation analysis of parallel spike trainsLecture recording during the workshop Advanced Neural Data Analysis (ANDA) 2019. |
Institution(s) |
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Lecturer(s) | Sonja Grün | |
Time / ETCS | / | |
Weblink | Visit the course website |
Berlin | Additional Topics | Block course |
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Ethics and NeurosciencePhilosophical theories of ethics, mental privacy, ethical aspects of animal experiments, ethical aspects of clinical neuroscience and patient research, good scientific practice, data protection and computer security, neurolaw, ethics committees. |
Type of the course |
Lecture, Group Work, block |
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Target audience | MSc, PhD | |
Prerequisites |
none |
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Institution(s) |
BCCN Berlin, Berlin School of Mind and Brain, Berlin |
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Time / ETCS | Winter, semester break, 1 week / 2-3 ECTS | |
Additional info |
International Winter School |
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Weblink | Visit the course website |
Berlin | Additional Topics | Block course |
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Mathematics Prep-CourseThis course is intended as a refreshment of mathematical tools of analysis, linear algebra and statistics which will be necessary for the CNS students in the first year. Students will acquire broadmathematical knowledge of functions in one resp. several real variables, in linear algebra, in differential equations, in probability theory and statistics, as needed for Computational Neuroscience. Basic mathematical skills for the analysis and approximation of functions, solutions of differential equations and signals, for solving linear systems and systems of ordinary differential equations will be refreshed. |
Type of the course |
Lecture, block |
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Target audience | MSc | |
Prerequisites |
Maths: 3 semesters |
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Institution(s) |
BCCN Berlin |
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Lecturer(s) | Schwalger | |
Time / ETCS | Winter, semester break, 2 weeks / 4 ECTS | |
Additional info |
Two-week course before new MSc students start their first term (Sep-Oct) |
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Weblink | Visit the course website |
Berlin | Additional Topics | Block course |
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Neurobiology Prep-CourseThis course is intended as bridge for students without physiology training, enrolling in Computational Neuroscience. The aim is to provide the basics in neurophysiology. |
Type of the course |
Lecture, block |
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Target audience | MSc | |
Prerequisites |
Maths: none |
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Institution(s) |
BCCN Berlin |
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Lecturer(s) | Larkum | |
Time / ETCS | Winter, semester break, 9 days / 2 ECTS | |
Additional info |
One-week course before new MSc students start their first term (Sep-Oct) |
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Weblink | Visit the course website |
Berlin | Data Analysis | Running lecture |
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Machine IntelligenceCourse description:
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Type of the course |
Lecture Maths Tutorial, weekly |
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Target audience | MSc, PhD | |
Prerequisites |
Maths: 3 semesters |
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Institution(s) |
Technische Universität Berlin, BCCN Berlin, Berlin |
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Lecturer(s) | Obermayer | |
Time / ETCS | Winter and summer, 2 x 4h per week / 2 x 6 ECTS | |
Additional info |
Runs for 2 semesters, each semester can be taken separately for 6 ECTS |
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Weblink | Visit the course website |
Berlin | Theoretical Neuroscience | Running lecture |
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Acquisition and Analysis of Neural DataStudents will gain knowledge about the most important methods for experimental acquisition of neural data and the respective analytical methods, they will learn about the different fields of application, the advantages and disadvantages of the different methods and will become familiar with the respective raw data. They will be enabled to choose the most appropriate analysis method and apply them to experimental data. |
Type of the course |
Analytical / Programming Tutorial Practical Course (Lab) |
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Target audience | MSc, PhD | |
Prerequisites |
This module is compulsory for students of the Master program Computational Neuroscience, compulsory elective or elective for the specialization Computational Neuroscience, Artificial Intelligence, and Signal Processing (generally for advanced Diploma students or master students). |
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Institution(s) |
BCCN Berlin |
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Lecturer(s) | Haynes et al. | |
Time / ETCS | Winter and summer, weekly/ 2 x 4 h per week / 5 - 12 ECTS | |
Additional info |
Runs for 2 semesters, possible combination would be the winter term course with focus on data acquisition, 5 ECTS without the project |
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Weblink | Visit the course website |
Berlin | Theoretical Neuroscience | Running lecture |
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Models of Higher Brain FunctionsParticipants should learn the basic concepts and most important topics in the Cognitive Neurosciences. In addition, they should know the state-of-the-art models in these domains and their theoretical foundations. After completing the module, participants should understand strengths and limitations of the different modeling approaches (e.g. bottom-up versus top-down), should be able to understand the rationale behind models and their implementation, and should be aware of performance criteria and critical statistical tests. Participants should also be able to modify models of cognitive processes as well as to apply existing models to novel experimental paradigms, situations or data. |
Type of the course |
Lecture, Analytical Tutorial, Programming Tutorial |
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Target audience | MSc, PhD | |
Prerequisites |
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Institution(s) | ||
Lecturer(s) | Haynes and Sprekeler | |
Time / ETCS | 2 h weekly in winter- and 6 h weekly in summer semester / 2-12 | |
Additional info |
Starts with seminar “Cognitive Neuroscience” in Winter Semester (2 ECTS), the rest runs during the summer term, multiple combinations possible This year: No course in summer semester 2019, summer course will be postponed to winter semester 2019/20! |
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Weblink | Visit the course website |
Berlin | Theoretical Neuroscience | Running lecture |
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Models of Neural SystemsParticipants should learn basic concepts, their theoretical foundation, and the most common models used in computational neuroscience. The module also provides the relevant basic neurobiological knowledge and the relevant theoretical approaches as well as the findings resulting form these approaches so far. After completing the module, participants should understand strengths and limitations of the different models. Participating students will learn to appropriately choose the theoretical methods for modeling neural systems. They will learn how to apply these methods while taking into account the neurobiological findings, and they should be able to critically evaluate results obtained. Participants should also be able to adapt models to new problems as well as to develop new models of neural systems. |
Type of the course |
Theoretical Lecture, Experimental Lecture, Analytical Tutorial, Computer Tutorial |
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Target audience | MSC, PhD | |
Prerequisites |
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Institution(s) | ||
Lecturer(s) | Lindner | |
Time / ETCS | Winter term /weekly/8 h per week / 12 | |
Additional info |
Combinations of a subset of the courses for fewer ECTS possible |
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Weblink | Visit the course website |
Berlin | Theoretical Neuroscience | Running seminar |
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Stochastic Partial Differential EquationsParticipants should learn basic concepts, their theoretical foundation, and the most common models of stochastic evolution equations on Hilbert spaces with a view towards its applications to the modelling, analysis and numerical approximation of spatially extended neurons and neural systems subject to noise. Participants will learn basic techniques to analyze global properties of neural systems both qualitatively and quantitatively. Participants will also learn basic simulation techniques for stochastic neural systems and how to evaluate simulation output. Participants should also be able to adapt models to new problems as well as to develop new models of neural systems. |
Type of the course |
Seminar |
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Target audience | MSc, PhD | |
Prerequisites |
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Institution(s) | ||
Lecturer(s) | Stannat | |
Time / ETCS | Summer term /weekly/2 h per week / 3 ECTS | |
Weblink | Visit the course website |
Berlin | Theoretical Neuroscience | Running lecture |
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Stochastic Processes in NeuroscienceParticipants should learn basic concepts, their theoretical foundation, and the most common models of stochastic processes used in computational neuroscience to model noisy neural systems. Participants will learn basic techniques to analyze the stochastic behavior of singles neurons and neural systems both qualitatively and quantitatively. Participants will also learn basic simulation techniques for stochastic neural systems and how to evaluate simulation output. Participants should also be able to adapt models to new problems as well as to develop new models of neural systems. |
Type of the course |
Lecture |
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Target audience | MSc, PhD | |
Prerequisites |
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Institution(s) | ||
Lecturer(s) | Stannat | |
Time / ETCS | Summer term / weekly/4 h per week / 6 ECTS | |
Weblink | Visit the course website |
Bochum | Additional Topics | Lab Rotation |
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Modeling Episodic Memory and Spatial NavigationThe computational neuroscience group at the Ruhr University Bochum studies the cognitive and neural mechanisms underlying episodic memory and spatial navigation. The group employs a wide range of computational methods ranging from (spiking) neural networks, abstract cognitive models to machine learning algorithms such as unsupervised and reinforcement learning. |
Target audience | MSc | |
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Prerequisites |
Python |
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Institution(s) |
Institut für Neuroinformatik, Ruhr-Universität Bochum |
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Lecturer(s) | Cheng et al. | |
Time / ETCS | Winter or summer, individually planned / no ECTS | |
Weblink | Visit the course website |
Bremen | Additional Topics | Block seminar |
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Neurophysics (Advanced Studies I)Practical course on neuronal network modeling and data analysis. Problems selected from the fields of neuronal coding, neuronal decoding, data analysis and small neuronal networks will be treated in small projects. The focus lies on the development of simple, yet viable models and the performance of computer simulations required for obtaining significant results. |
Type of the course |
Practical Course, |
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Target audience | Master | |
Prerequisites |
Maths: 1 semester |
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Institution(s) |
University of Bremen |
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Lecturer(s) | Ernst, Pawelzik et al. | |
Time / ETCS | 20.04.2020 - 15.05.2020, 08:00 - 18:00h / 9 ECTS | |
Additional info |
For more information contact Udo Ernst (udo - at - neuro.uni-bremen.de) |
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Weblink | Visit the course website |
Bremen | Additional Topics | Running lecture |
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ProgrammingLearn to write your own computer programs to analyse data and simulate neuronal systems. In the first half of the lectures and practical exercises, you will achieve the basic skills to write computer programs which perform simple calculations, and we will advise you how to break down a more complex problem into simple tasks a computer can perform. In the second half of the course, you will apply your acquired skills to analyse neural signals (mean and variance, estimation of firing rates, reverse correlation, ROC analysis, etc.), and simulate single neurons or synapses (integrate-and-fire neuron, Hodgkin-Huxley neuron). |
Type of the course |
Lecture, Programming Tutorial |
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Target audience | Master, (Bachelor) | |
Prerequisites |
Maths: none |
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Institution(s) |
University of Bremen |
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Lecturer(s) | Erhard et al. | |
Time / ETCS | Lectures and exercises 2 semester hours per week: Block course from Nov. 4 to Nov. 22, 2019, Thuesday and Thursday, 14:00-16:00 and Dec. 2 to Dec. 20, Wednesday and Thursday, 14:00-16:00, 2019, / 3 ECTS | |
Weblink | Visit the course website |
Bremen | Theoretical Neuroscience | Running lecture |
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Theoretical Neurosciences: Computational Neuroscience IIntroduction to fundamental concepts in Computational Neuroscience. In the first term, we will study basic encoding and decoding schemes, analysis of neural signals, and the dynamics of single neurons. In the second term, we will focus on synapses and neural networks, and study emergent phenomena such as computation and classification, learning and memory, pattern formation, and synchronization. |
Type of the course |
Lecture, Math exercises, |
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Target audience | Master, (Bachelor) | |
Prerequisites |
Maths: 1 semester |
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Institution(s) |
University of Bremen |
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Lecturer(s) | Ernst et al. | |
Time / ETCS | Winter, starting on 5th of November 2019 / 2 ECTS | |
Additional info |
Lecture and exercises 2 semester hours per week: Block course from Dec. 2, 2019 to Jan. 24, 2020 Tuesday and Friday, 14:00-16:00 |
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Weblink | Visit the course website |
Bremen | Theoretical Neuroscience | Running lecture |
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Theoretical Neurosciences: Computational Neuroscience IIFollow-up to course "Computational Neuroscience I" held in Winter Semester: Here we will focus on synapses and neural networks, and study emergent phenomena such as computation and classification, learning and memory, pattern formation, and synchronization. |
Type of the course |
Lecture, |
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Target audience | Master, (Bachelor) | |
Prerequisites |
Maths: 1 sem. |
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Institution(s) |
University of Bremen |
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Lecturer(s) | Ernst, Pawelzik et al. | |
Time / ETCS | Summer, weekly, 2h per week, every Friday lecture 2-4 pm, exercises 12-2 pm / 2 ECTS | |
Additional info |
Prepatory meeting: Friday, 17.04.2020 For more information contact Udo Ernst udo - at - neuro.uni-bremen.de |
Cologne | Data Analysis | Block course |
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Advanced Course on Neural Data AnalysisThis advanced course aims at providing deeper insights in state-of-the-art questions in neuroscience, analysis approaches and how to formalize questions to neuronal data so they can be answered quantitatively. The course addresse ecellent master studetns and PhD students interested in data analytics and in getting hands-on experience in the analysis of electrophysiological data (multiple-parallel spike trains and local field potentials). In the first week of the course, international experts will give lectures on statistical data analysis and data mining methods with accompanied exercises. In the second week the participants will pursue their own data analysis projects on a common data set. |
Type of the course |
Lectures, Tutorial, Exercises, |
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Target audience | MSc, PhD | |
Prerequisites |
Good programming skills, background in mathematics (algebra) and statistics |
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Institution(s) |
Jülich Research Center, University of Cologne, LMU Munich/German Neuroinformatics Node/ |
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Lecturer(s) | Organizers: Gruen, Nawrot, Wachtler et al. | |
Time / ETCS | Spring, block, 2.5 weeks, 14.04.-2020-30.04.2020 / 6 ECTS | |
Additional info |
Deadline for application is November 15, 2019. |
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Weblink | Visit the course website |
Cologne | Data Analysis | Block course |
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Introduction to Scientific Programming in Python with Application to Neural Data AnalysisThis module will equip the student with basic skills of scientific programming with PYTHON and provide the student with hands-on experience in the statistical analysis of experimental neurophysioloigcal data sets and the adequate presentation of results. No previous programming skills are required. |
Type of the course |
Tutorials, Exercises, block |
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Target audience | MSc | |
Prerequisites |
no background required |
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Institution(s) |
University of Cologne - Institute of Zoology |
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Lecturer(s) | Rostami, Nawrot | |
Time / ETCS | Feb 10-18, 2020; daily 9:30 am – 17:00 pm / 3 ECTS | |
Additional info |
Registration (2 places available): vrostami@uni-koeln.de Deadline: Dec 20, 2019 |
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Weblink | Visit the course website |
Jülich | Data Analysis | Online |
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ANDA: Cortial variability dynamics experimental observations and mechanistic modelsThis lecture was recorded during the Workshop "Advanced Neural Data Analysis Course" (ANDA) 2019. |
Institution(s) |
University of Cologne |
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Lecturer(s) | Martin Nawrot | |
Time / ETCS | / | |
Weblink | Visit the course website |
Darmstadt | Computational Modelling | Running seminar |
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Applied Cognitive ModelingAdvanced introduction to the implementation of cognitive models; levels of description: computational, algorithmic, and implementational models; reading, understanding, and implementing recent publications involving cognitive models, e.g. human information processing, decoding of physiological signals, artificial cognitive systems, machine learning in psychology, motor control. |
Type of the course |
Seminar and Programming Lab, weekly |
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Prerequisites |
Programming languages: e.g. Python, Matlab, BUGS/JAGS/Stan, R |
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Institution(s) |
Technische Universität Darmstadt |
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Lecturer(s) | Rothkopf et al. | |
Time / ETCS | Winter, weekly / 6 ECTS | |
Weblink | Visit the course website |
Frankfurt | Additional Topics | Running lecture |
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Systems Engineering Meets Life Sciences IThis multi-semester course focuses on emerging interdisciplinary |
Type of the course |
Lecture + exercise, weekly |
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Institution(s) |
Goethe University, Frankfurt |
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Lecturer(s) | Ramesh et al. | |
Time / ETCS | Winter, 4h per week / 6 ECTS | |
Additional info |
The course can be offered as a blended learning course with a distributed team project executed as a block course for one to two weeks at the end of the semester, too. |
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Weblink | Visit the course website |
Frankfurt | Computational Modelling | Running lecture |
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Machine Learning ISupervised, unsupervised and semi-supervised learning, reinforcement |
Type of the course |
Lecture and Exercise, weekly |
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Target audience | MSc, PhD | |
Prerequisites |
Basic linear algebra and programming skills |
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Institution(s) |
Goethe University, Frankfurt |
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Lecturer(s) | Kaschube, Ramesh et al. | |
Time / ETCS | Winter, 4h per week / 6 ECTS | |
Weblink | Visit the course website |
Frankfurt | Computational Modelling | Block course |
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Models for Neural Circuit DevelopmentWe will discuss the principles guiding the formation of sensory maps and receptive fields during circuit development. The students will examine how different mechanisms including: emergence of diverse single neuron properties and activity-dependent synaptic plasticity interact during development to give rise to functional circuits. The students will have the opportunity to analyze data from visual cortex and build their own models of the assembly and tuning of developing neuronal circuits. |
Type of the course |
Research module, block |
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Target audience | MSc or PhD | |
Prerequisites |
Programming languages: Matlab, C, Python |
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Institution(s) |
Max Planck Institute for Brain Research, Frankfurt |
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Lecturer(s) | Gjorgjieva et al. | |
Time / ETCS | Winter and summer, 4-6 weeks / | |
Weblink | Visit the course website |
Frankfurt | Theoretical Neuroscience | Running lecture |
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Theoretical NeuroscienceThis module provides an introduction to modern theoretical neuroscience with an attempt to cover all relevant spatial scales (from molecules to brain areas) as well as temporal scales (sub- millisecond to evolutionary times scales). |
Type of the course |
Lecture and exercise, weekly |
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Target audience | Bachelor, MSc, PhD | |
Prerequisites |
Basic analysis, linear algebra and programming skills |
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Institution(s) |
Goethe University Frankfurt |
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Lecturer(s) | Kaschube et al. | |
Time / ETCS | Weekly, 4h per week / 6 ECTS | |
Weblink | Visit the course website |
Giessen | Additional Topics | Running seminar |
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Sensation and PerceptionThis is an introductory-level seminar series on the psychology and biology of visual perception. Topics include illusions, retina, contours, colour, motion, Gestalt psychology, faces and objects, attention and consciousness, computer vision, eye movements, neuropsychology and art. Each week 2-3 students present one paper each for 20 minutes. After each presentation there is a question and answer and discussion section. Each student can also receive a 30-min tutorial with me a few days before their presentation. Participants receiving five or more CP must also complete an essay on the same topic as their presentation. |
Type of the course |
Seminar, weekly |
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Target audience | MSc | |
Institution(s) |
Justus-Liebig-Universität, Department of Psychology (FB6), Giessen |
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Lecturer(s) | Fleming et al. | |
Time / ETCS | Winter or Summer, weekly, 2h per week / 3 - 5 ECTS | |
Giessen | Computational Modelling | Lab Rotation |
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Masters Thesis Project on Visual PerceptionMasters projects for motivated students seeking a career in research are available in Roland Fleming's lab. Research in the lab focusses on mid- and high-level vision, including the perception of 3D shape (e.g. shape-from-shading) and the physical properties of objects and materials (e.g. viscosity, stiffness). We make extensive use of photorealistic computer graphics and simulations. The project would combine psychophysics with modeling (e.g. deep learning). |
Type of the course |
Lab Exchange, Master Thesis Project |
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Target audience | MSc | |
Prerequisites |
Maths: 1 semester, Programming: fluent, Neuroscience: basic, Programming languages: Matlab and/or Python. Familiarity with Caffe is an advantage. |
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Institution(s) |
Justus-Liebig-Universität, Department of Psychology (FB6), Giessen |
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Lecturer(s) | Fleming | |
Time / ETCS | 2 Semesters / | |
Göttingen | Data Analysis | Lab Rotation |
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Information Dynamics/Information theoretic analysis of neural dataStudents will learn to estimate active information storage, transfer and modifcation using the IDTxl toolbox. datasets will be electrophysiological recordings from non-human primates and MEG data from patients with Autism. |
Institution(s) |
Max Planck Institute for Dynamics and Self Organization - Departement of Nonlinear Dynamics & Network Dynamics Group |
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Lecturer(s) | Michael Wibral | |
Time / ETCS | all year, minimum of 8 weeks / | |
Weblink | Visit the course website |
Göttingen | Theoretical Neuroscience | Block seminar |
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Neural Networks and Information ProcessingAn introductory meeting will take place on Wednesday, 24th of October at 12:15 at the MPI for Dynamics and Self-Organization, room 0.77 |
Type of the course |
Block seminar, 1 week |
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Institution(s) |
Max Planck Institute for Dynamics and Selforganization, Göttingen |
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Lecturer(s) | Priesemann, Levina et al. | |
Time / ETCS | 18.02.2019 to 22.02.2019, Introductionary meeting on 24.10.2018 / | |
Göttingen | Theoretical Neuroscience | Lab Rotation |
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Nonlinear dynamicsCollective dynamics; subsampling; information theory; synaptic plasticity; principles of local learning. |
Prerequisites |
Programming or analytical skills are required |
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Institution(s) |
Max-Planck Institute for Dynamics and Self-Organization - Departement of Nonlinear Dynamics & Network Dynamics Group, Göttingen |
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Lecturer(s) | Viola Priesemann | |
Time / ETCS | Time and duration depend on topic / | |
Weblink | Visit the course website |
Göttingen | Theoretical Neuroscience | Running lecture |
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Theoretical and Computational Neuroscience: Collective dynamics of biological neural Networks IIIntroduction to Neurophysics, Modeling and Methods, Nonlinear Dynamics, Statistical Physics, Neurobiology, Neural Networks. This lecture course offers an introduction to advanced modeling strategies for biological neural networks. After a short introduction to the biophysics of single cells and an overview of their basic firing patterns, we explain fundamental properties of networks models of neurons, starting from simple uniform connectivity and progressing to spatially extended and to arbitrarily complex interaction networks. These network models explain and predict key dynamical aspects of neural circuits, including irregular activity of cortical dynamics, feature selectivity, self-organization of neural maps, and the coordination of precisely timed spikes across networks. The summer term course has its focus on neural field models. |
Type of the course |
Lecture |
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Target audience | MSc or BSc (Physics, Mathematics, Applied Informatics) | |
Institution(s) |
Institute für Nichtlineare Dynamik, departement of physics, Univesity of Göttingen |
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Lecturer(s) | Wolf, Priesemann et al. | |
Time / ETCS | Summer, weekly, 2h per week / 3 ECTS | |
Additional info |
Language: English |
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Weblink | Visit the course website |
Hamburg | Additional Topics | Online |
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SFB 936 ONLINE COURSE "NETWORK NEUROSCIENCE"The small private online course (SPOC) "Network Neuroscience" provides a broad overview on recent research regarding Network Neuroscience with the following topics presented by members of the SFB 936:
Here are some formal information about the online course:
Participation and registration is free of charge, but registration requires:
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Type of the course |
Seminar recordings |
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Target audience | MSc, PhD | |
Prerequisites |
none |
|
Institution(s) |
University Medical Center Hamburg-Eppendorf (UKE), Hamburg |
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Lecturer(s) | Trautmann-Lengsfeld | |
Time / ETCS | weekly, Semester is flexible / | |
Additional info |
Please contact Sina A. Trautmann-Lengsfeld (s.trautmann-lengsfeld@uke.de) via email including the above information. We look forward to hearing from you.
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Weblink | Visit the course website |
Jülich | Additional Topics | Running lecture |
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Statistical PhysicsStatistical theory of equilibrium states (e.g. collective phenomena, phase transitions, renormalization group theory) and nonequilibrium processes (e.g. kinetic theory, stochastic processes). Students learn the principles, on which the modeling of complex systems is based, as well as the mathematical methods that are employed in deriving and solving such models. |
Type of the course |
lecture and exercise, weekly |
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Institution(s) |
RWTH Aachen University |
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Lecturer(s) | Helias | |
Time / ETCS | Summer, 4h per week / | |
Weblink | Visit the course website |
Jülich | Data Analysis | Running lecture |
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Statistical mechanics of neural networksThe neural networks of the brain form one of the most complex systems we know. Many qualitative features of the emerging collective phenomena, such as correlated activity, stability, response to inputs, chaotic and regular behavior, can, however, be understood in simple models that are accessible to a treatment in statistical mechanics. This course presents the fundamentals behind contemporary developments in neural network theory that are based on methods from statistical mechanics of classical systems with a large number of interacting degrees of freedom. |
Type of the course |
Lecture and Exercise, weekly |
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Institution(s) |
RWTH Aachen |
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Lecturer(s) | Helias | |
Time / ETCS | Winter, 3h per week / | |
Weblink | Visit the course website |
Jülich | Theoretical Neuroscience | Running lecture |
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Introduction to Computational NeuroscienceModels of neurons, synapses and networks; concepts of neuronal coding and cortical information processing; plasticity and learning. Data analysis and visualization by self-written programs; Usage of scientific programming languages (Matlab and Python), also for documenting the analyses; hypothesis tests by numerically generated modified data ('surrogate data'); Simulation of neuronal circuits. |
Type of the course |
Lecture and exercise, weekly |
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Target audience | MSc (biology, physics, ect.) | |
Prerequisites |
mathematical background; considerable advantage: programm skills |
|
Institution(s) |
RWTH Aachen University, Jülich |
|
Lecturer(s) | Grün et al. | |
Time / ETCS | Winter, 3h per week / biology: 6 ECTS; math: 7,5 ECTS; physics: 10 ECTS | |
Additional info |
This course is taught in combination with "Cortical Structure and Function" taught during the winter semester. This course can also function as Computational Neuroscience (I), if "Cortical Structure and Function" is used as (II). However, I is not a prerequisite for II. |
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Weblink | Visit the course website |
Jülich | Theoretical Neuroscience | Running lecture |
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Theoretical Neuroscience – Correlation structure of neuronal networks
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Type of the course |
lecture and exercise, weekly, starting 09.04.2020 |
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Institution(s) |
RWTH Aachen University |
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Lecturer(s) | Diesmann, Helias | |
Time / ETCS | Weekly: Thursday, 16:30 - 19:00 / | |
Weblink | Visit the course website |
Leipzig | Theoretical Neuroscience | Running lecture |
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Mathematical topics in the neurosciencesIn this course, I plan to treat the dynamics of populations of neurons and of synaptic learning. |
Type of the course |
Lecture, weekly |
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Target audience | MSc, PhD | |
Prerequisites |
some background in mathematics |
|
Institution(s) |
Max Planck Institute for Mathematics in the Sciences, Leipzig |
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Lecturer(s) | Jürgen Jost | |
Time / ETCS | Winter, weekly, 2h per week / | |
Additional info |
Language: English Starting Nov 01, 2019 |
|
Weblink | Visit the course website |
Magdeburg | Theoretical Neuroscience | Running lecture |
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Spiking NetworksSpecial mathematical and statistical tools are required to model and analyze biologically plausible networks of spiking neurons. The course offers a step-by-step introduction to stochastic dynamical systems up to and including current mean-field approaches to recurrent networks of spiking neurons. Stochastic variables, stochastic processes, interval distributions, autocorrelation and power spectrum, Wiener process and white noise, LIF neurons with Poisson input, diffusion limit, Fokker-Planck equation of VIF neuron, mean-field theory of recurrently connected populations, application to decision making and confidence. In the practical part, students simulate and analyze recurrent networks of spiking neurons on the basis of Matlab and Neuron. |
Type of the course |
Lecture, exercise, tutorial, weekly |
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Target audience | MSc, PhD | |
Prerequisites |
MSc level algebra and calculus, Matlab programming |
|
Institution(s) |
Institute for Biology, OvGU Magdeburg |
|
Time / ETCS | Winter, weekly, 2h per week / 4 ECTS | |
Weblink | Visit the course website |
Magdeburg | Theoretical Neuroscience | Running lecture |
---|---|---|
Theoretical neuroscience IBased on Chapters 5-6 and Chapters 1-4 of Dayan & Abbott. Electrochemical equilibrium and Nernst Equation, equivalent circuits for single-compartment model, leaky integrate-and-fire model, Hodgkin-Huxley and Connor-Stevens models of action potential, cable equation and neuron morphology, characterizing neuronal responses with tuning curves and receptive fields, signal- detection theory and psychometric function, comparison of neuronal and behavioural responses with neurometric function, population coding, statistically efficient decoding with maximum likelihood and maximum a posteriori likelihood, introduction to Shannon information, application of Shannon information to neural responses. |
Type of the course |
Lecture, exercise, tutorial, weekly |
|
---|---|---|
Target audience | MSc, PhD | |
Prerequisites |
BSc level algebra and calculus, Matlab programming |
|
Institution(s) |
Institute for Biology, OvGU Magdeburg |
|
Time / ETCS | Winter, 2h+2h+2h per week / 5 ECTS | |
Weblink | Visit the course website |
Freiburg | Additional Topics | Online |
---|---|---|
MidsummerBrains: Computational neuroscience and field biologyComputational neuroscience is a highly interdisciplinary field ranging from mathematics, physics and engineering to biology, medicine and psychology. Interdisciplinary collaborations have resulted in many groundbreaking innovations both in the research and application. The basis for successful collaborations is the ability to communicate across disciplines: What projects are the others working on? Which techniques and methods are they using? How is data collected, used and stored? In the MidsummerBrains colloquium, experts from the SMARTSTART faculty describe their view on computational neuroscience in theory and application, and share experiences they had with interdisciplinary projects. This is a recording of the lecture "There and back again: Computational neuroscience and field biology", held by Jan Benda, head of the department of neuoethology at the University of Tübingen (Dep. of Neuroethology). Jan Benda talked about his point of view on computational neuroscience, the tricky life beyond lab conditions and quite explicitly talking fish. |
Type of the course |
Online lecture, colloquium |
|
---|---|---|
Target audience | All students of computational neuroscience | |
Prerequisites |
None |
|
Institution(s) |
SMARTSTART in collaboration with the University of Tübingen |
|
Lecturer(s) | Jan Benda | |
Time / ETCS | / | |
Weblink | Visit the course website |
Freiburg | Additional Topics | Online |
---|---|---|
MidsummerBrains: Engineers in Computational NeuroscienceComputational neuroscience is a highly interdisciplinary field ranging from mathematics, physics and engineering to biology, medicine and psychology. Interdisciplinary collaborations have resulted in many groundbreaking innovations both in the research and application. The basis for successful collaborations is the ability to communicate across disciplines: What projects are the others working on? Which techniques and methods are they using? How is data collected, used and stored? In the MidsummerBrains colloquium, experts from the SMARTSTART faculty describe their view on computational neuroscience in theory and application, and share experiences they had with interdisciplinary projects. This is a recording of the lecture "My View on Computational Neuroscience", held by Stefan Glasauer, head of the department of computational neuroscience at the Brandenburg University of Technology (b-tu.de/en/).
In this lecture, he gives insights on his career from early interests in nature to engineering and how different views can help to solve the bigger problems. |
Type of the course |
Online lecture, colloquium |
|
---|---|---|
Target audience | All students of computational neuroscience | |
Prerequisites |
None |
|
Institution(s) |
SMARTSTART in collaboration with the Brandenburg University of Technology |
|
Lecturer(s) | Stefan Glasauer | |
Time / ETCS | / | |
Weblink | Visit the course website |
Freiburg | Additional Topics | Online |
---|---|---|
MidsummerBrains: Physics and computational neuroscienceComputational neuroscience is a highly interdisciplinary field ranging from mathematics, physics and engineering to biology, medicine and psychology. Interdisciplinary collaborations have resulted in many groundbreaking innovations both in the research and application. The basis for successful collaborations is the ability to communicate across disciplines: What projects are the others working on? Which techniques and methods are they using? How is data collected, used and stored? In the MidsummerBrains colloquium, experts from the SMARTSTART faculty describe their view on computational neuroscience in theory and application, and share experiences they had with interdisciplinary projects. This is a recording of the lecture "My View on Computational Neuroscience", held by Tatjana Tchumatchenko, head of the theory of neural dynamics group at the MPI for Brain Research in Frankfurt (tchumatchenko.de/). She talked about her point of view on computational neuroscience as a physicist and the winded path from experiment to insight and publication. |
Type of the course |
Online lecture, colloquium |
|
---|---|---|
Target audience | All students of computational neuroscience | |
Prerequisites |
None |
|
Institution(s) |
SMARTSTART in collaboration with the MPI for Brain Research Frankfurt |
|
Lecturer(s) | Tatjana Tchumatchenko | |
Time / ETCS | / | |
Weblink | Visit the course website |
Freiburg | Additional Topics | Online |
---|---|---|
MidsummerBrains: Quantifying nonlinear representations in the visual systemsComputational neuroscience is a highly interdisciplinary field ranging from mathematics, physics and engineering to biology, medicine and psychology. Interdisciplinary collaborations have resulted in many groundbreaking innovations both in the research and application. The basis for successful collaborations is the ability to communicate across disciplines: What projects are the others working on? Which techniques and methods are they using? How is data collected, used and stored? In the MidsummerBrains colloquium, experts from the SMARTSTART faculty describe their view on computational neuroscience in theory and application, and share experiences they had with interdisciplinary projects. This is a recording of the lecture "Quantifying nonlinear representations in the visual systems", held by Fabian Sinz, group leader of the neuronal intelligence lab in Tübingen as part of the CyberValley initiative (sinzlab.org/). He is talking about the difficulties of nonlinear representations and what black boxes mean for machine learning. |
Type of the course |
Online lecture, colloquium |
|
---|---|---|
Target audience | All students of computational neuroscience | |
Prerequisites |
None |
|
Institution(s) |
SMARTSTART in collaboration with the University of Tübingen |
|
Lecturer(s) | Fabian Sinz | |
Time / ETCS | / | |
Weblink | Visit the course website |
Freiburg | Additional Topics | Online |
---|---|---|
MidsummerBrains: The Virtual BrainComputational neuroscience is a highly interdisciplinary field ranging from mathematics, physics and engineering to biology, medicine and psychology. Interdisciplinary collaborations have resulted in many groundbreaking innovations both in the research and application. The basis for successful collaborations is the ability to communicate across disciplines: What projects are the others working on? Which techniques and methods are they using? How is data collected, used and stored? In the MidsummerBrains colloquium, experts from the SMARTSTART faculty describe their view on computational neuroscience in theory and application, and share experiences they had with interdisciplinary projects. This is a recording of the lecture "The Virtual Brain - Improving Life through Simulation", held by Petra Ritter from the Charité Berlin on June 24th, 2019. |
Type of the course |
Online lecture, colloquium |
|
---|---|---|
Target audience | All students of computational neuroscience | |
Prerequisites |
None |
|
Institution(s) |
SMARTSTART in collaboration with Charité Berlin |
|
Lecturer(s) | Petra Ritter | |
Time / ETCS | / | |
Weblink | Visit the course website |
Munich | Additional Topics | Running seminar |
---|---|---|
Advanced Seminar in Computational NeuroscienceSpecial topics in computational neuroscience, incl. talks by guest speakers, review of new publications in the field and progress report on ongoing research projects |
Type of the course |
Seminar, weekly |
|
---|---|---|
Target audience | MSc, PhD | |
Institution(s) |
LMU, Munich |
|
Lecturer(s) | Herz, Leibold et al. | |
Time / ETCS | Winter and summer, 2h per week / 2 ECTS | |
Additional info |
Mo, 10:30 - 12:00 |
|
Weblink | Visit the course website |
Munich | Additional Topics | Online |
---|---|---|
Communication AcousticsIn this engineering course, we will cover all aspects of communication acoustics, which is the way sounds travels from a source, through a channel and finally to a receiver. We will look at the different system components involved in acoustic communication, including those between humans, between humans and machines, and between machines. This includes:
You will learn from top experts in the field of communication acoustics, who are all affiliated with TU9, the nine leading Universities of Technology in Germany. Together, they have pooled their expertise in order to teach a comprehensive basic understanding and indicate current research trends to you. |
Type of the course |
Lecture with integrated exercises, weekly |
|
---|---|---|
Target audience | MSc, PhD | |
Institution(s) |
TUM, Munich |
|
Lecturer(s) | Seeber et al. | |
Time / ETCS | Winter, 2h+2h per week / 6 ECTS | |
Additional info |
video-course, online |
|
Weblink | Visit the course website |
Munich | Additional Topics | Running lecture |
---|---|---|
NeuroprostheticsThe lecture covers the theoretical foundations of neuroprostheses. As the underlying principle of all neuroprostheses is the electrical excitation of neurons, we will cover this topic in depth using cochlea implants as an example. In the practical computer laboratory (2SWS), which complements |
Type of the course |
Lecture with integrated exercises, weekly |
|
---|---|---|
Target audience | MSc, PhD | |
Institution(s) |
TUM, Munich |
|
Lecturer(s) | Hemmert et al. | |
Time / ETCS | Winter, 4h per week / | |
Weblink | Visit the course website |
Munich | Additional Topics | Running lecture |
---|---|---|
Psychoacoustics and Audiological ApplicationsBinaural hearing: binaural cues, masking, directional hearing, movement perception, precedence effect, models; Hearing impairment: Kinds of hearing impairment, frequency selectivity and auditory filters, masking and across-frequency processes, loudness and recruitment, temporal and |
Type of the course |
Lecture with integrated exercises, weekly |
|
---|---|---|
Target audience | MSc, PhD | |
Institution(s) |
TUM, Munich |
|
Lecturer(s) | Seeber et al. | |
Time / ETCS | Winter, 2h+2h per week / 6 ECTS | |
Additional info |
Lecture held in English |
|
Weblink | Visit the course website |
Munich | Additional Topics | Running seminar |
---|---|---|
Spatial and Temporal CognitionThe hippocampus and surrounding medial temporal lobe (MTL) contain cell types that represent place, time and are important for memory. Both brain regions are conserved across species implying general function. For years this function has been suggested in pattern separation, pattern completion, and attractor dynamics that would be necessary for memory. This winter term we first examine recent evidence for pattern completion, pattern separation and attractor dynamics based on the anatomy and circuitry of the hippocampus. Later we will broaden the view and look at papers on how hippocampus and MTL are related to time and space. |
Type of the course |
Seminar, weekly |
|
---|---|---|
Target audience | MSc, PhD | |
Institution(s) |
LMU, Munich |
|
Lecturer(s) | Flanagin, Thurley | |
Time / ETCS | Wednesdays 10-12 c.t. / 3 ECTS | |
Weblink | Visit the course website |
Munich | Data Analysis | Running lecture |
---|---|---|
Fundamentals of Computer Science for NeuroengineeringIntroduction to computer science, computer programming, and data |
Type of the course |
Lecture, weekly |
|
---|---|---|
Target audience | MSc, PhD | |
Institution(s) |
TUM, Munich |
|
Lecturer(s) | Macke et al. | |
Time / ETCS | Winter, 4h per week / 5 ECTS | |
Weblink | Visit the course website |
Munich | Data Analysis | Running lecture |
---|---|---|
Fundamentals of Mathematics for NeuroengineeringFor further information please contact the lecturer Jakob Macke |
Type of the course |
Lecture, weekly |
|
---|---|---|
Target audience | MSc, PhD | |
Institution(s) |
TUM, Munich |
|
Lecturer(s) | Macke et al. | |
Time / ETCS | Winter, 4h per week / 5 ECTS | |
Weblink | Visit the course website |
Munich | Data Analysis | Block seminar |
---|---|---|
Practical Course Methods in Functional ImagingThe goal of this practical course is to give students the tools, knowledge and hands-onexperience needed to plan, conduct and analyse a task-based fMRI or PET experiment. In the first week of the course, there will be theoretical lectures on data acquisition and analysis as well as guided tutorials on how to analyse fMRI and structural MRI data with SPM12 and Melodic in FSL. The tutorials are self-paced. In the second week, you will be asked to analyse a data set on your own and write a short report to be handed in at the end of the course. |
Type of the course |
Practical course, block |
|
---|---|---|
Target audience | MSc, PhD | |
Institution(s) |
LMU, Munich |
|
Lecturer(s) | Flanagin et al. | |
Time / ETCS | 20.01.-31.01.2020 / 3 ECTS | |
Additional info |
Registration per email: neuroimaging@med.uni-muenchen.de |
|
Weblink | Visit the course website |
Munich | Data Analysis | Block seminar |
---|---|---|
Practical Short Course Methods for Computational NeuroscienceFor further information please contact the lecturer Christian Leibold. |
Type of the course |
Practical course, block |
|
---|---|---|
Target audience | MSc, PhD | |
Institution(s) |
LMU, Munich |
|
Lecturer(s) | Leibold et al. | |
Time / ETCS | 13.01.-17.01.2020 / | |
Weblink | Visit the course website |
Munich | Theoretical Neuroscience | Running lecture |
---|---|---|
Computational Neuroscience: A Lecture Series from Models to ApplicationsInterdisciplinary lecture series taught by neuroscience experts from TUM and LMU that provides an introduction to computational neuroscience. |
Type of the course |
Lecture, weekly |
|
---|---|---|
Target audience | MSC, PhD | |
Institution(s) |
LMU, TUM, Munich |
|
Lecturer(s) | Herz, Luksch, Seeber, Thurley et al. | |
Time / ETCS | Winter 2018/19 + Summer 2019 / 3 ECTS | |
Weblink | Visit the course website |
Munich | Theoretical Neuroscience | Running lecture |
---|---|---|
Fundamentals in NeuroscienceThe lecture gives an introduction to the following topics: Neurons & glia, passive membrane properties, ion channels, cation potentials, synaptic transmission, transmitter systems, cellular networks, motor systems, learning and memory, sensory systems, orientation, echolocation. |
Type of the course |
Lecture and exercise, weekly |
|
---|---|---|
Target audience | MSc, PhD | |
Institution(s) |
LMU, Munich |
|
Lecturer(s) | Busse, Grothe et al. | |
Time / ETCS | Winter, weekly, 2h + 2h per week / 5 ECTS | |
Weblink | Visit the course website |
Munich | Theoretical Neuroscience | Running lecture |
---|---|---|
The Neural CodeFor more information please contact the lecturers Leibold and Wachtler |
Type of the course |
Lecture and exercise, weekly |
|
---|---|---|
Target audience | MSc, PhD | |
Institution(s) |
LMU, Munich |
|
Lecturer(s) | Leibold, Wachtler et al. | |
Time / ETCS | Winter, weekly, 2h + 2h per week / 3 ECTS | |
Weblink | Visit the course website |
Munich | Theoretical Neuroscience | Running lecture |
---|---|---|
Theoretical Biophysics and Cellular PhysiologyThis course covers the mathematical foundations of cellular physiology, ranging from the ionic basis of the membrane potential to electrochemical signaling to the propagation of action potentials in axons and dendrites of neurons based on the Hodgkin-Huxley model of the squid giant axon. |
Type of the course |
Lecture and exercise, weekly |
|
---|---|---|
Target audience | MSc, PhD | |
Institution(s) |
LMU, Munich |
|
Lecturer(s) | Leibold, Borst et al. | |
Time / ETCS | Winter, weekly, 2h + 2h per week / | |
Weblink | Visit the course website |
Oldenburg | Data Analysis | Block course |
---|---|---|
Computational Neuroscience - IntroductionThis intense block course provides some theoretical background, |
Type of the course |
block course, including lecture, seminar, hands-on programming |
|
---|---|---|
Target audience | MSc, PhD | |
Prerequisites |
programming experience (preferably Matlab) |
|
Institution(s) |
University of Oldenburg, MSc Neuroscience, Oldenburg |
|
Lecturer(s) | Kretzberg, Hildebrandt, Greschner, Ashida | |
Time / ETCS | Winter, 6 weeks full-day, Dec-Jan / 12 ECTS | |
Weblink | Visit the course website |
Oldenburg | Data Analysis | Block course |
---|---|---|
Computational Neuroscience - Statistical LearningThis intense block course provides some theoretical background, extensive hands-on programming exercises in Matlab and interpretation of the obtained modeling results. Multi-channel neurophyisological data analysis: Data pre-processing, data analysis toolboxes, theory of multi-dimensional statistical methods, matlab implementation, visualization |
Type of the course |
block course, including lecture, seminar, hands-on programming exercises |
|
---|---|---|
Target audience | MSc, PhD | |
Prerequisites |
Matlab programming experience, |
|
Institution(s) |
University of Oldenburg, MSc Neuroscience |
|
Lecturer(s) | Kretzberg et al. | |
Time / ETCS | Summer, full-day block, 1.4.-26.4.2019 / 6 ECTS | |
Additional info |
Course is held in English |
|
Weblink | Visit the course website |
Oldenburg | Data Analysis | Lab Rotation |
---|---|---|
Individual Project in Auditory Neuroscience groupStudents perform an individual research project (lab rotation) in the Auditory Neuroscience Group. Topics and methods depend on the individual interests and backgrounds of the students. Vertebrate auditory system: Animal behavior, electrophysiology, anatomy, or data analysis project. |
Type of the course |
individual student research project, lab rotation |
|
---|---|---|
Target audience | MSc, PhD | |
Prerequisites |
Matlab |
|
Institution(s) |
University of Oldenburg, MSc Neuroscience |
|
Lecturer(s) | Hildebrandt | |
Time / ETCS | Winter and summer, 6-8 weeks, flexible timing / 15 ECTS | |
Additional info |
Course is held in English |
Oldenburg | Data Analysis | Lab Rotation |
---|---|---|
Individual Project in Computational Neuroscience groupStudents perform an individual research project (lab rotation) in the Computational Neuroscience Group. Topics and methods depend on the individual interests and backgrounds of the students. Invertebrate (leech) mechanosensory system: Electrophysiology, data analysis or modeling project. (Intracellular recordings and simulation techniques can be learned during the project.) Vertebrate auditory system: Modeling project. |
Type of the course |
individual student research project, lab rotation |
|
---|---|---|
Target audience | MSc, PhD | |
Prerequisites |
Matlab |
|
Institution(s) |
University of Oldenburg, MSc Neuroscience |
|
Lecturer(s) | Kretzberg | |
Time / ETCS | Winter and summer, 6-8 weeks, flexible timing / 15 ECTS | |
Additional info |
Course is held in English |
|
Weblink | Visit the course website |
Oldenburg | Data Analysis | Block course |
---|---|---|
Introduction to Biological Data Analysis in MatlabIntroduction to Matlab programming for students with little or no programming experience. In hands-on exercises, students first practice to use basic programming concepts (scripts and functions, data flow and control flow, loops, structures and cell arrays…) and then apply their knowledge to statistical analysis of biological (including neuroscientific) data. After the block course, students work independently on a programming task used for evaluation. |
Type of the course |
block course, including lecture, seminar, hands-on programming exercises |
|
---|---|---|
Target audience | BSc, MSc, PhD | |
Prerequisites |
Programming language used: Matlab |
|
Institution(s) |
University of Oldenburg, 'Professionalisierungsbereich' |
|
Lecturer(s) | Kretzberg | |
Time / ETCS | Summer, full-day block, 3 weeks in August / September / 6 ECTS | |
Additional info |
Course is held in German |
|
Weblink | Visit the course website |
Oldenburg | Data Analysis | Block course |
---|---|---|
Invertebrate NeuroscienceThis background module in neurophysiology consists of three weeks of seminar and hands-on lab exercises on intracellular recordings from leech neurons, as well as computer simulations to study the basis of membrane potential and action potential generation. The seminar covers the following topics: |
Type of the course |
block course, including lecture, seminar, hands-on wet-lab and simulation exercises |
|
---|---|---|
Target audience | MSc, PhD | |
Prerequisites |
Matlab would be helpful |
|
Institution(s) |
University of Oldenburg, MSc Neuroscience |
|
Lecturer(s) | Kretzberg | |
Time / ETCS | Summer, full-day block, 27.5.-14.6.2019 / 6 ECTS | |
Additional info |
Course is held in English |
|
Weblink | Visit the course website |
Oldenburg | Data Analysis | Block course |
---|---|---|
Neuroscientific Data Analysis in MatlabIntroduction to Matlab, Readable Code: good practice, Data Import, basic statistics, frequency analysis, Continuous data (EEG/LFP), Behavioral data, Flow control, Spike data, Logical Indexing, complex data types, Variables and their scope, Advanced plotting & Matrix operations, Images, Object-oriented programming, Individual projects, Version control, Wrap up, Principles of data analysis. |
Type of the course |
lecture and exercises |
|
---|---|---|
Target audience | MSc, PhD | |
Institution(s) |
University of Oldenburg, MSc Neuroscience, Oldenburg |
|
Lecturer(s) | Jannis Hildebrandt | |
Time / ETCS | Winter, 7 weeks part-time, Oct - Nov / 6 ECTS | |
Additional info |
Course is held in English |
Magdeburg | Theoretical Neuroscience | Online |
---|---|---|
Online Lecture: Theoretical Neuroscience IHere you find recordings of the course Theoretical Neuroscience I, held by Jochen Braun at the University of Magdeburg. Individual topics:
To go to directly to a specific lecture, right click the link (blue) and choose "open in new tab". |
Institution(s) |
University of Magdeburg |
|
---|---|---|
Lecturer(s) | Jochen Braun | |
Time / ETCS | / | |
Weblink | Visit the course website |
Magdeburg | Theoretical Neuroscience | Online |
---|---|---|
Online Lecture: Theoretical Neuroscience IIHere you find recordings of the course Theoretical Neuroscience II, held by Jochen Braun at the University of Magdeburg. Individual topics:
|
Institution(s) |
University of Magdeburg |
|
---|---|---|
Lecturer(s) | Jochen Braun | |
Time / ETCS | / | |
Weblink | Visit the course website |
Osnabrück | Additional Topics | Running seminar |
---|---|---|
Action & Cognition IIn this lecture, and its follow-up Action & Cognition II in the summer term, we discuss the physiological substrate of cognitive processes with an emphasis on their relation to behavior. On your journey through the brain we will meet object recognition, attention, decision processes, movement planning and consciousness. A bias will be on physiological mechanisms, but due attention to clinical aspects, theoretical analysis and information theoretic measures will be given. |
Type of the course |
Seminar, weekly |
|
---|---|---|
Target audience | Master | |
Prerequisites |
Statistics, basic mathematics and programming skills |
|
Institution(s) |
Institute of Cognitive Science, |
|
Lecturer(s) | König et al. | |
Time / ETCS | Winter, weekly, 2 h/week + Tutorials / 4 ECTS | |
Weblink | Visit the course website |
Osnabrück | Additional Topics | Running seminar |
---|---|---|
Action & Cognition IIIn this lecture, it is the follow-up of Action & Cognition I in the winter term, we discuss the physiological substrate of cognitive processes with an emphasis on their relation to behavior. On your journey through the brain we will meet object recognition, attention, decision processes, movement planning and consciousness. A bias will be on physiological mechanisms, but due attention to clinical aspects, theoretical analysis and information theoretic measures will be given. |
Type of the course |
Seminar, weekly |
|
---|---|---|
Target audience | Master | |
Prerequisites |
Statistics, basic mathematics and programming skills |
|
Institution(s) |
Institute of Cognitive Science, |
|
Lecturer(s) | König et al. | |
Time / ETCS | Summer, weekly, 2 h/week + Tutorials / 4 ECTS | |
Additional info |
Action & Cognition I recommended |
|
Weblink | Visit the course website |
Osnabrück | Additional Topics | Running seminar |
---|---|---|
Cognitive Human-Computer InteractionThe course focuses on the cognitive basis relevant for the design of user interfaces, the development of user interfaces, and usability aspecs of user interfaces. |
Type of the course |
Seminar, weekly |
|
---|---|---|
Target audience | Master | |
Prerequisites |
Statistics, basic mathematics and programming skills |
|
Institution(s) |
Institute of Cognitive Science, Osnabrück |
|
Lecturer(s) | Kühnberger et al. | |
Time / ETCS | Winter, weekly, 2h per week / 4 ECTS | |
Weblink | Visit the course website |
Osnabrück | Additional Topics | Running seminar |
---|---|---|
Colloquium of the Institute of Cognitive ScienceVarious topics |
Type of the course |
Colloquium, weekly |
|
---|---|---|
Target audience | Bachelor, Master | |
Prerequisites |
Statistics, basic mathematics and programming skills |
|
Institution(s) |
Institute of Cognitive Science/ |
|
Lecturer(s) | Jäkel, König et al. | |
Time / ETCS | weekly, 2h per week / 2 ECTS | |
Weblink | Visit the course website |
Osnabrück | Additional Topics | Block course |
---|---|---|
Colloquium of the PhD ProgrammeVarious topics |
Type of the course |
Colloquium, weekly |
|
---|---|---|
Target audience | PhD | |
Prerequisites |
Statistics, basic mathematics and programming skills |
|
Institution(s) |
Institute of Cognitive Science, Osnabrück |
|
Lecturer(s) | Pipa et al. | |
Time / ETCS | weekly, 2h per week / 4 ECTS | |
Weblink | Visit the course website |
Osnabrück | Computational Modelling | Running seminar |
---|---|---|
Bayesian StatisticsBayesian statistics and modeling, JAGS |
Type of the course |
Seminar, weekly |
|
---|---|---|
Target audience | Master, PhD | |
Prerequisites |
Statistics, basic mathematics and programming skills |
|
Institution(s) |
Institute of Cognitive Science, Osnabrück |
|
Lecturer(s) | Pipa et al. | |
Time / ETCS | Summer, weekly, 2h per week / 4 ECTS | |
Weblink | Visit the course website |
Osnabrück | Data Analysis | Running seminar |
---|---|---|
Colloquium Computer VisionThe course starts with the basics of image processing and proceeds to computer vision, A focus is on object recognition. |
Type of the course |
Colloquium, weekly |
|
---|---|---|
Target audience | Bachelor, Master | |
Prerequisites |
Statistics, basic mathematics and programming skills |
|
Institution(s) |
Institute of Cognitive Science/ |
|
Lecturer(s) | Heidemann et al. | |
Time / ETCS | weekly, 2h per week / 8 + 4 ECTS | |
Weblink | Visit the course website |
Osnabrück | Data Analysis | Running lecture |
---|---|---|
Machine Learning IThe course gives an introduction to unsupervised and supervised techniques of machine learning and data mining: Decision trees, clustering, dimension reduction, classification and artificial neural networks. |
Type of the course |
Lecture + Practice |
|
---|---|---|
Target audience | Bachelor, Master | |
Prerequisites |
Statistics, basic mathematics and programming skills |
|
Institution(s) |
Institute of Cognitive Science, |
|
Lecturer(s) | Heidemann et al. | |
Time / ETCS | weekly, 2h+2h+2h per week / 8 + 4 ECTS | |
Weblink | Visit the course website |
Osnabrück | Data Analysis | Running seminar |
---|---|---|
Machine Learning IIImportant frameworks for advanced methods in Machine Learning are discussed in this course. This includes e.g. SVMs, probabilisitic methods, and reinforcement learning. |
Type of the course |
Seminar, weekly |
|
---|---|---|
Target audience | Master, PhD | |
Prerequisites |
Statistics, basic mathematics and programming skills |
|
Institution(s) |
Institute of Cognitive Science, Osnabrück |
|
Lecturer(s) | Kühnberger et al. | |
Time / ETCS | weekly, 2h+2h+2h per week / 4 ECTS | |
Weblink | Visit the course website |
Osnabrück | Theoretical Neuroscience | Running lecture |
---|---|---|
Neuro Dynamics (alternating with Neuro-informatics)Modelling spiking neurons and network, principles of complex systems |
Type of the course |
Lecture + Seminar, |
|
---|---|---|
Target audience | Master, PhD | |
Prerequisites |
Statistics, basic mathematics and programming skills |
|
Institution(s) |
Institute of Cognitive Science, |
|
Lecturer(s) | Pipa et al. | |
Time / ETCS | weekly, 2h+2h per week / 8+4 ECTS | |
Additional info |
alternating with Neuro-informatics |
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Weblink | Visit the course website |
Osnabrück | Theoretical Neuroscience | Running lecture |
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Neuro-InformaticsIn this lecture, we will discuss cutting edge approaches from the field of Neuroinformatics. The aim of the lecture is to get the students familiar with the concept of modelling and abstracting data, and the up to date knowledge about computational processes in the brain. After a short introduction that covers probability theory, and linear models for regression and classification, we will start a journey through the fields of graphical models and liquid computing. In the last part of the lecture we will conclude with an outlook to self-organization with the purpose to optimize information processing in complex systems like the brain. To link the knowledge acquired in this course with scientific question every second lecture a 30 min 3W session is offered. The three big W are: why should I learn this / what for can I use it / how can it be important my bachelor and master thesis . The lecture will be supplemented by a block seminar on decoding neuronal activity at the beginning of the semester break. |
Type of the course |
Lecture + Seminar, |
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Target audience | Master | |
Prerequisites |
Statistics, basic mathematics and programming skills |
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Institution(s) |
Institute of Cognitive Science, Osnabrück |
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Lecturer(s) | Pipa et al. | |
Time / ETCS | weekly, 2h+2h per week / 12+4 ECTS | |
Additional info |
alternating with Neuro Dynamics |
|
Weblink | Visit the course website |
Osnabrück | Theoretical Neuroscience | Running lecture |
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Probabilistic Modeling of Perception and CognitionProbability theory, judgment and decision making, choice models, signal detection theory |
Type of the course |
Lecture and Tutorial |
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Target audience | Master | |
Prerequisites |
Statistics, basic mathematics and programming skills |
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Institution(s) |
Institute of Cognitive Science, |
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Lecturer(s) | Jäkel et al. | |
Time / ETCS | weekly, 2h+2h per week / 8 ECTS | |
Weblink | Visit the course website |
Tübingen | Additional Topics | Running lecture |
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Neural Experimental TechniquesThis course will provide a detailed overview of the experimental methods currently used in the Neurosciences to record (as well as modulate) neuronal activity – from the local activity with single-synapse resolution to population activity at the level of brain areas. |
Type of the course |
Lecture, weekly |
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Target audience | Master, PhD | |
Prerequisites |
Math: none |
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Institution(s) |
Graduate Training Center of Neuroscience, Tübingen |
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Lecturer(s) | Euler, Zeck | |
Time / ETCS | Winter, 2h per week / 3 ECTS | |
Weblink | Visit the course website |
Tübingen | Data Analysis | Running lecture |
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Machine Learning IThis course will provide an introduction to important topics and algorithms in machine learning. A particular focus of this course will be on algorithms that have a clear statistical (and often Bayesian) interpretation. |
Type of the course |
Lecture, weekly |
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Target audience | Master, PhD | |
Prerequisites |
Math: 3 semesters |
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Institution(s) |
Graduate Training Center of Neuroscience, Tübingen |
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Lecturer(s) | Dijkstra | |
Time / ETCS | Winter, 2h per week / 4 ECTS | |
Weblink | Visit the course website |
Tübingen | Data Analysis | Running lecture |
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Machine Learning IIIn this course, students will learn about important topics and techniques in machine learning, with a particular focus on probabilistic models. The course will cover supervised learning (linear regression algorithms, linear discriminants, logistic regression, nonlinear classification algorithms) and unsupervised learning (principal component analysis including several generalizations, k-means, mixture of Gaussians, Expectation-Maximization) |
Type of the course |
Lecture and exercises, weekly |
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Target audience | Bachelor, Master | |
Prerequisites |
basic knowledge of linear algebra and probability theory, |
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Institution(s) |
Graduate Training Center of Neuroscience, Tübingen |
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Lecturer(s) | Dijkstra | |
Time / ETCS | Summer, 3h per week / 5 ECTS | |
Weblink | Visit the course website |
Tübingen | Theoretical Neuroscience | Running lecture |
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Models of neural systemsThe lecture introduces models of neurons of different complexity from the detailed Hodgkin-Huxley models for action potential generation via |
Type of the course |
Lecture + math tuorial with exercises, weekly |
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Target audience | Master, Bachelor | |
Prerequisites |
Math 1 semester, programming none, neuroscience basic |
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Institution(s) |
Institute for Neurobiology, University of Tübingen |
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Lecturer(s) | Benda | |
Time / ETCS | Winter, weekly, 4h per week / 6 ECTS | |
Additional info |
The lecture and exercises are tailored to biologists |
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Weblink | Visit the course website |
Tübingen | Theoretical Neuroscience | Running lecture |
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Neural DynamicsThis course treats the basic biophysics of the signal generation and |
Type of the course |
Lecture, weekly |
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Target audience | Master, PhD | |
Prerequisites |
Math: 3 semesters |
|
Institution(s) |
Graduate Training Center of Neuroscience, Tübingen |
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Lecturer(s) | Giese | |
Time / ETCS | Winter, weekly, 2h per week / 6 ECTS | |
Weblink | Visit the course website |